Emma:An accurate,efficient,and multi-modality strategy for autonomous vehicle angle prediction  

作　　者：Keqi Song Tao Ni Linqi Song Weitao Xu 

机构地区：[1]Shenzhen Research Institute,City University of Hong Kong,Hong Kong,China,and also with the Department of Computer Science,City University of Hong Kong,Hong Kong,China

出　　处：《Intelligent and Converged Networks》2023年第1期41-49,共9页智能与融合网络（英文）

基　　金：supported by the National Natural Science Foundation of China(No.62101471);partially supported by the Shenzhen Research Institute of City University of Hong Kong,the Research Grants Council of the Hong Kong Special Administrative Region,China(No.CityU 21201420);Shenzhen Science and Technology Funding Fundamental Research Program(No.2021Szvup126);National Natural Science Foundation of Shandong Province(No.ZR2021LZH010);Changsha International and Regional Science and Technology Cooperation Program(No.kh2201023);Chow Sang Sang Group Research Fund sponsored by Chow Sang Sang Holdings International Limited(No.9229062);CityU MFPRC(No.9680333);CityU SIRG(No.7020057);CityU APRC(No.9610485);CityU ARG(No.9667225);CityU SRG-Fd(No.7005666).

摘　　要：Autonomous driving and self-driving vehicles have become the most popular selection for customers for their convenience.Vehicle angle prediction is one of the most prevalent topics in the autonomous driving industry,that is,realizing real-time vehicle angle prediction.However,existing methods of vehicle angle prediction utilize only single-modal data to achieve model prediction,such as images captured by the camera,which limits the performance and efficiency of the prediction system.In this paper,we present Emma,a novel vehicle angle prediction strategy that achieves multi-modal prediction and is more efficient.Specifically,Emma exploits both images and inertial measurement unit(IMU)signals with a fusion network for multi-modal data fusion and vehicle angle prediction.Moreover,we design and implement a few-shot learning module in Emma for fast domain adaptation to varied scenarios(e.g.,different vehicle models).Evaluation results demonstrate that Emma achieves overall 97.5%accuracy in predicting three vehicle angle parameters(yaw,pitch,and roll),which outperforms traditional single-modalities by approximately 16.7%-36.8%.Additionally,the few-shot learning module presents promising adaptive ability and shows overall 79.8%and 88.3%accuracy in 5-shot and 10-shot settings,respectively.Finally,empirical results show that Emma reduces energy consumption by 39.7%when running on the Arduino UNO board.

关 键 词：MULTI-MODALITY autonomous driving vehicle angle prediction few-shot learning 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置]